System and method for improved energy series of images using multi-energy ct

ABSTRACT

A method for creating an energy series of images acquired using a multi-energy computed tomography (CT) imaging system having a plurality of energy bins includes acquiring, with the multi-energy CT imaging system, a series of energy data sets, where each energy data set is associated with at least one of the energy bins. The method includes producing a conglomerate image using at least a plurality of the energy data sets and, using the conglomerate image, reconstructing an energy series of images, each image in the energy series of images corresponding to at least one of the energy data sets.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is based on, claims the benefit of, andincorporates herein by reference U.S. Provisional Application Ser. No.61/365,191, filed Jul. 16, 2010, and entitled, System and Method forImproved Energy Series of Images using Multi-Energy CT.

BACKGROUND OF THE INVENTION

The present invention relates to computed tomography (CT) imaging and,more particularly, to systems and methods for energy domain datacorrection in spectral CT imaging to control noise and radiation dose.

In a computed tomography system, an x-ray source projects a fan or coneshaped beam which is collimated to lie within an X-Y plane of aCartesian coordinate system, termed the “imaging plane.” The x-ray beampasses through the object being imaged, such as a medical patient orother non-medical patient or object, such as in industrial CT imaging,and impinges upon an array of radiation detectors. The intensity of thetransmitted radiation is dependent upon the attenuation of the x-raybeam by the object and each detector produces a separate electricalsignal that is a measurement of the beam attenuation. The attenuationmeasurements from all the detectors are acquired separately to producethe transmission profile at a particular view angle.

The source and detector array in a conventional CT system are rotated ona gantry within the imaging plane and around the object so that theangle at which the x-ray beam intersects the object constantly changes.A group of x-ray attenuation measurements from the detector array at agiven angle is referred to as a “view”, and a “scan” of the objectcomprises a set of views acquired at different angular orientationsduring one revolution of the x-ray source and detector. In a 2D scan,data is processed to construct an image that corresponds to a twodimensional slice taken through the object. The prevailing method forreconstructing an image from 2D data is referred to in the art as thefiltered backprojection technique, however, other image reconstructionprocesses are also well known. This process converts the attenuationmeasurements from a scan into integers called “CT numbers” or“Hounsfield units”, which are used to control the brightness of acorresponding pixel on a display.

The term “generation” is used in CT to describe successivelycommercially available types of CT systems utilizing different modes ofscanning motion and x-ray detection. More specifically, each generationis characterized by a particular geometry of scanning motion, scanningtime, x-ray beam shape, and detector system.

The first generation utilized a single linear x-ray beam (“pencil beam”)and a single scintillation crystal-photomultiplier tube detector foreach tomographic slice. After a single linear motion or traversal of thex-ray tube and detector, during which time 160 separate x-rayattenuation or detector readings are typically taken, the x-ray tube anddetector are rotated through one degree and another linear scan isperformed to acquire another view. This is repeated typically to acquire180 views.

A second generation of CT systems was developed to shorten the scanningtimes of first generation systems by gathering the attenuation data morequickly. In these units, a modified fan beam, including anywhere from3-52 individual collimated x-ray beams, and a number of detectors equalto the number of collimated x-ray beams are used. Individual beamsresemble the single beam of a first generation scanner; however, acollection of from 3-52 of these beams contiguous to one another allowsmultiple adjacent regions of tissue to be examined simultaneously. Theconfiguration of these contiguous regions of tissue resembles a fan,with the thickness of the fan material determined by the collimation ofthe beam and in turn determining the slice thickness. Because of theangular difference of each beam relative to the others, severaldifferent angular views through the body slice are being examinedsimultaneously. Superimposed on this is a linear translation or scan ofthe x-ray tube and detectors through the body slice. Thus, at the end ofa single translational scan, during which time 160 readings may be madeby each detector, the total number of readings obtained is equal to thenumber of detectors times 160. The increment of angular rotation betweenviews can be significantly larger than with a first generation unit, upto as much as 36°. Thus, the number of distinct rotations of thescanning apparatus can be significantly reduced, with a coincidentalreduction in scanning time. By gathering more data per translation,fewer translations are needed.

To obtain even faster scanning times it is necessary to eliminate thecomplex translational-rotational motion of the first two generations.Third generation scanners therefore use a much wider, “divergent” fanbeam. In fact, the angle of the beam may be wide enough to encompassmost or all of an entire patient section without the need for a lineartranslation of the x-ray tube and detectors. As in the first twogenerations, the detectors, now in the form of a large array, arerigidly aligned relative to the x-ray beam, and there are notranslational motions at all. The tube and detector array aresynchronously rotated about the patient through an angle of 180-360°.Thus, there is only one type of motion, allowing a much faster scanningtime to be achieved. After one rotation, a single tomographic section isobtained.

Fourth generation scanners also feature a divergent fan beam similar tothe third generation CT system. As before, the x-ray tube rotatesthrough 360° without having to make any translational motion. However,unlike in the other scanners, the detectors are not aligned rigidlyrelative to the x-ray beam. In this system only the x-ray tube rotates.A large ring of detectors are fixed in an outer circle in the scanningplane. The necessity of rotating only the tube, but not the detectors,allows faster scan time.

Beyond these large “generational” distinctions between CT technology, anumber of additional advancements have been made. For example, dualenergy and even dual source CT systems have been developed. In eithercase, x-ray dose of different energy levels are used to acquire twoimage data sets from which a low energy and a high energy image may bereconstructed. As will be described, a wide variety of information canthan be determined from the subject by analyzing the characteristics andvariations between the low energy data set and the high energy data set.

In addition, photon counting (PC) and energy discriminating (ED)detector CT systems have the potential to greatly increase the medicalbenefits of CT. Unlike the above-described “traditional” CT detectors,which integrate the charge generated by x-ray photon interactions in thedetector but provide no specific energy information regarding individualphotons, PC detectors record the energy deposited by each individualphoton interacting with the detector. PC detector system can provide newclinical abilities due to an ability to differentiate materials such asa contrast agent in the blood and calcifications that may otherwise beindistinguishable in traditional CT systems. Also, they can improve thesignal to noise ratio (SNR) by reducing electronic and swank noise. PCand ED CT systems generally produce less image noise for the same dosethan photon energy integrating detectors and hence can be more doseefficient than conventional CT systems. Also, they can improve SNR byassigning optimal, energy dependent weighting factors to the detectedphotons and achieve additional SNR improvements by completely orpartially rejecting scattered photons. Further still, PC detectors allowmeasurement of transmitted, energy-resolved spectra from a singleexposure at one tube potential.

The development of PC detectors for micro-CT and whole-body CTapplications has enabled a new dimension of CT imaging, namely “spectralCT” or “multi-energy CT.” These advances have attracted considerableattention in the scientific and research communities, due to thepotential for enhanced material characterization utilizing spectralx-ray information. This lays the groundwork for many clinicalapplications, such as detecting new biomarkers, such as iron invulnerable plaque, multi-contrast imaging, such as iodine and bariumimaging of the bowel luminal wall and intra-lumen contents, andexploring intrinsic tissue contrast, cancerous tissue compared to normaltissue.

In contrast to conventional CT systems, where photons are measured andrecorded in a single transmission data set, spectral CT generatesmultiple data sets, with each data set measuring only those photons withenergies between predefined low and high energy thresholds. Because thex-ray attenuation of a material depends on the photon energy,material-specific information is then built into each energy-specificdata set. Measured data from each energy bin is then reconstructedindependently to generate a series of CT images, each corresponding to aspecific energy range. These images are highly correlated, since theanatomic geometry and physical density of the object remains unchangedfor any time point. Only the total x-ray attenuation values, that is, CTnumbers, differ, according to the material type and selected photonenergy bin. An attenuation-energy curve can be generated from thesemultiple image series, each image corresponding to one energy bin. Sinceeach material has its own attenuation-energy curve, materialidentification/differentiation can then be achieved using multi-energyCT.

The appropriate selection of energy bins, for example, the number ofenergy bins and width of each energy bin, has a significant affect onthe outcome of spectral imaging. A narrow energy bin has better energyresolution compared to a wider energy bin, and hence enables bettermaterial identification/differentiation. For example, FIG. 1 shows agraph of two energy bins used to separate iron, which is a biomarker forvulnerable plaques, from calcium. A narrow energy window width of 20 keVwas used and the two energy bins were widely separated in the x-rayspectrum. The dual energy ratio difference, which is an indicator ofmaterial separation capability, or the dual-energy “contrast” betweentwo materials, using these energy windows were compared withconventional dual energy CT, in which wider energy windows, with a lowof 0 to 80 kVp and an high of 0 to 140 kVp were used. Significantimprovement was observed using the narrow beam energy windows (20 keV).However, a significant limitation of using narrow bins is that thenumber of photons available in each energy bin is much smaller than thetotal number of photons detected. For the scenario in FIG. 1, only asmall portion of total photons were used in each energy-specific imageand a large fraction of photons in between energy bin 1 and 2 werediscarded.

As image noise is proportional to the inverse square root of availablephotons, image noise is correspondingly higher using a narrow energy binthan a wide energy bin. Thus, a critical problem occurs. Specifically,in order to identify or differentiate materials using spectral CT, thedifferences in effective atomic number or signal must be amplifiedby: 1) using narrow energy bins and 2) separating the energy bins aswidely as possible. However, this requirement excludes a largepercentage of the detected energy spectrum from the considered imagedata. Thus, the resultant images, in which dual-energy signal isincreased, suffer from increased noise. For narrow energy bins,especially in the lower energy range, the image noise may be so high asto make it impossible to detect small differences in materialcomposition, that is, the signal to noise ratio (SNR) is too low.Further, a large portion of the dose delivered to the patient is wasted,creating a difficult dilemma of the clinician balancing between dosedelivered and achieving a desired SNR. Thus, the requirements forincreasing dual-energy signal are in direct conflict with therequirements for decreasing image noise in the individual energy imagesand in any material composition images derived from the energy specificimages.

Similar observations exist for the selection of total number of energybins. For a given x-ray spectrum, a given kVp, more energy bins providemore measurements of energy dependent information. With multiple datapoints available along the attenuation-energy curve, bettercurve-fitting, consequently better material differentiation is achieved.However, more bins also dictates narrower widths for each bin and hencefewer photons in each bin. Turning to FIG. 2, a scenario in which 6energy bins were used is illustrated. In FIG. 2, 6 separate measurementsin the energy domain corresponding to the 6 energy bins are available.However, the number of photons in each energy bin is only 1/6 of thetotal photons delivered. Accordingly, the noise in each image is thensignificantly high.

Therefore, an intrinsic tradeoff exists in the selection of energy bins(number, width, and placement) for spectral CT, resulting in thedescribed tradeoff between energy-specific signal (materialidentification/differentiation information) and noise. This tradeofflimits the clinical applications of spectral CT. For example, for thedifferentiation between iron (a biomarker for plaque vulnerability) fromcalcium in vascular plaques, narrow energy bins are generally used dueto the very small concentration iron amidst a typically higherconcentration of calcium (i.e. there is a very weak signal). Due to thesmall signal size, image noise must be strictly controlled to allow thedetection of iron, and hence the identification of those plaques morelikely to rupture and cause acute myocardial infarction. Thus, a dilemmais presented of increasing signal size through appropriate selection ofthe energy bins is counterproductive due to the increase in image noise.Although increased photons (dose) could potentially be used, increasesin patient dose above existing levels will prevent clinical applicationdue to the heightened concern about ionizing radiation in medicine andpotential long-term effects of such radiation on patients. An increaseddose will also likely require higher power and cooling requirements onthe x-ray tube and generator, as current coronary CT angiography alreadyuses the upper limits of tube/generator technology. Addressing this withuse of longer scan times, such as using longer gantry rotation times,would sacrifice image quality with motion artifact and hence blur outthe small signal that is sought.

Accordingly, it would be desirable to have a system and method forcreating an energy series of images with reduced noise and increasedsignal to noise ratio.

SUMMARY OF THE INVENTION

The present invention overcomes the aforementioned drawbacks byproviding a system and method for creating an energy series of imagesacquired using a multi-energy computed tomography (CT) imaging systemhaving a plurality of energy bins. Using the multi-energy CT imagingsystem, a series of energy data sets is acquired, where each energy dataset is associated with at least one of the energy bins. A conglomerateimage is produced using a plurality of the energy data sets and, usingthe conglomerate image, an energy series of images is reconstructed,where each image in the energy series of images corresponds to at leastone of the energy data sets. Thus, the present invention seeks toexploit a correlation of information in the energy domain to reduceimage noise in each energy-specific image, not just in a processedmaterial-composition image. As such, the present invention improvesmaterial differentiation in spectral CT by allowing selection of desiredenergy bins and, in particular, the number of bins, the width of thebins, and the location of the bins, without paying a noise penalty.

In accordance with one aspect of the invention, a method for creating anenergy series of images acquired using a computed tomography (CT)imaging system is disclosed that includes acquiring a series ofenergy-selective data sets, each energy-selective data set associatedwith energy bin and producing a conglomerate data set from theenergy-selective data sets including data associated with at least aplurality of the energy bins. The method also includes weighting each ofthe energy-selective data sets using the conglomerate data set andreconstructing an enhanced energy series of images, where each image inthe enhanced energy series of images corresponds to at least one of theenergy data sets.

In accordance with another aspect of the invention, a method forcreating an energy series of images acquired using a multi-energycomputed tomography (CT) imaging system having a plurality of energybins is disclosed that includes acquiring a series of energy data sets,each energy data set associated with at least one of the energy bins.The method also includes producing a conglomerate data set using atleast a plurality of the energy data sets and using the conglomeratedata set, generating at least one of an enhanced material-specific imageand an enhanced energy series of images, each image corresponding to atleast one of the energy data sets.

In accordance with still another aspect of the invention, a computedtomography (CT) imaging system is disclosed that includes an x-raysource configured to emit x-rays toward an object to be imaged, adetector configured to receive x-rays that are attenuated by the object,and a data acquisition system (DAS) connected to the detector to receivean indication of received x-rays. The system also includes a computersystem coupled to the DAS to receive the indication of the receivedx-rays and programmed to segregate the indication of the received x-raysinto a series of energy data sets based on an energy level associatedwith received x-rays. The computer is further programmed to produce aconglomerate data set using data from at least a plurality of the energydata sets and reconstruct at least one of an enhanced material-specificimage and an enhanced energy series of images, each image in the atleast one of the enhanced material-specific image and enhanced energyseries of images corresponding to at least one of the energy data sets,using the series of energy data sets and the conglomerate data set.

Various other features of the present invention will be made apparentfrom the following detailed description and the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph illustrating energy with respect to two exemplary binsused with photon counting detectors and relative to a broadbrehmsstralung spectrum.

FIG. 2 is a graph illustrating that a dual-energy signal between ironand calcium is maximized with use of the two, widely separated energybins compared to energy windows currently used with integrating detectortechnology (80/140 kV=80 and 140 kVp beams or 80/140Sn=80 kVp and 140kVp beams, where the 140 kVp beam has been filtered with approximately0.4 mm of tin (Sn)) to selectively reduce the number of photons below 80keV, which increases the dual-energy signal due to the increasedseparation of mean beam energy.

FIG. 3 is pictorial view of a CT imaging system in which the presentinvention may be employed.

FIG. 4 is block schematic diagram of the CT imaging system of FIG. 3.

FIG. 5 is a flow chart setting forth exemplary steps of a method forcreating an enhanced series of images in accordance with the presentinvention.

FIG. 6 is a flow chart setting forth exemplary steps of a methodutilizing HYPR-based techniques to create an enhanced series of imagesin accordance with the present invention.

FIG. 7A is a series of images reconstructed without the use of aconglomerate image spanning all energy bins.

FIG. 7B is a series of images reconstructed using HYPR-LR to provide aconglomerate image spanning all energy bins and, thereafter, reconstructa series of energy images at 80, 100, 120, and 140 kVp.

FIG. 8 is a graph showing CT number and noise measured at ROIsrepresenting calcium, water, and soft tissue across a number ofreconstruction methods.

DETAILED DESCRIPTION OF THE INVENTION

With initial reference to FIGS. 3 and 4, a computed tomography (CT)imaging system 10 includes a gantry 12 representative of at least a“third generation” CT scanner. In the illustrated example, the gantry 12has a pair of x-ray sources 13 that each project a fan beam or cone beamof x-rays 14 toward a detector array 16 on the opposite side of thegantry 12. However, it is specifically noted that the present invention,while readily applicable to dual-source, dual-energy CT systems, is alsoreadily applicable to other multi-energy CT systems and methods, such assingle-source, dual- or multi-energy CT systems and methods. Thedetector array 16 is formed by a number of detector elements 18 thattogether sense the projected x-rays that pass through a medical patient15. As will be described, it is contemplated that the detector array 16may form part of a so-called “photon-counting” and/or“energy-discriminating” detector system. In any case, each detectorelement 18 produces an electrical signal that represents the intensityof an impinging x-ray beam or, more accurately, each photon or bunch ofphotons, and hence the attenuation of the beam (or photons) as it passesthrough the patient. During a scan to acquire x-ray projection data, thegantry 12 and the components mounted thereon rotate about a center ofrotation 19 located within the patient 15 to acquire attenuation data.

The rotation of the gantry and the operation of the x-ray source(s) 13are governed by a control mechanism 20 of the CT system. The controlmechanism 20 includes an x-ray controller 22 that provides power andtiming signals to the x-ray sources 13 and a gantry motor controller 23that controls the rotational speed and position of the gantry 12. A dataacquisition system (DAS) 24 in the control mechanism 20 samples analogdata from detector elements 18 and converts the data to digital signalsfor subsequent processing. An image reconstructor 25, receives sampledand digitized x-ray data from the DAS 24 and performs high speed imagereconstruction. The reconstructed image is applied as an input to acomputer 26 which stores the image in a mass storage device 28.

The computer 26 also receives commands and scanning parameters from anoperator via console 30 that has a keyboard. An associated display 32allows the operator to observe the reconstructed image and other datafrom the computer 26. The operator supplied commands and parameters areused by the computer 26 to provide control signals and information tothe DAS 24, the x-ray controller 22, and the gantry motor controller 23.In addition, computer 26 operates a table motor controller 34 thatcontrols a motorized table 36 to position the patient 15 in the gantry12.

In CT scans, image noise is highly correlated to the number of photonsreceived. Thus, lower noise in a resulting image is achieved when morex-ray photons are used to create the image. For this reason, traditionalnotions of CT imaging focus including all usable x-ray information.However, spectral CT imaging diverts from this notion by segregating theinformation into bins. The present invention recognizes that spectral CTis, in essence, an imaging technique in four dimensions. In particular,spectral CT deals with the three dimensions in space and a uniquedimension in energy. Within this conceptual context, the presentinvention recognizes that time-resolved spectral CT includes a 5^(th)dimension, namely a dimension of time.

The present invention builds on the above recognition that spectral CTis, in essence, an imaging technique in four dimensions (or fivedimensions, in the case of a time series of images) and furtherrecognizes that a high degree of correlation exists between theenergy-specific data sets of spectral CT imaging due to the fact thatthe data sets pertain to the same patient anatomy. Using theserecognitions, the present invention exploits this correlation ofinformation in the energy domain to reduce image noise in eachenergy-specific image, not just in a processed material compositionimage. That is, although extensive research has been conducted in thespatial and temporal domains to reduce noise and improve image quality,generally, limited investigation has been done in energy domain.Currently, data acquired from each energy bin in the energy domain istreated independently and CT images at each energy utilize only datafrom a single energy bin. As will be described, the present inventiondiverts from this traditional notion and ultimately improves materialdifferentiation in spectral CT by allowing selection of the optimal ordesired energy bins and, in particular, the number of bins, the width ofthe bins, and the location of the bins, without paying a substantialnoise penalty.

In x-ray CT, image noise is inversely related to the square root of thetotal number of photons used to reconstruct the image. The number ofphotons associated with each energy bin image in spectral CT is reducedfor the same patient exposure because of dividing the total number ofphotons applied to the patient into multiple energy bins to obtainenergy specific information. Noise level therefore increases compared toconventional CT images that use all available photons. The degree ofnoise increase depends on the number of energy bins and the width ofeach energy bin. By utilizing the redundant information in the energydomain, image noise in spectral CT can be reduced to the level ofconventional CT.

Thus, the present invention recognizes that, in the case of multi-energyCT imaging, images reconstructed from all received photons can betreated as “conventional” CT images that have little or no materialdifferentiating information (energy-resolved signal), but which alsohave the lowest noise and use the full dose applied to the patient.Comparing these “conventional” images reconstructed from all receivedphotons and images reconstructed from each energy bin in a multi-energyacquisition illustrates that the images are not independent. Rather, theimages actually have a high degree of correlation due to the fact thatthey measure the same anatomy. The present invention exploits thiscorrelation to generate new energy-specific, CT image sets that havedramatically reduced noise levels, such as is generally achievable withconventional CT images, yet maintain the CT numbers of individual energybin images. These images retain each individual data set's specificenergy signature, while noise can be reduced to as low as that of theconventional image that is reconstructed directly from all acquiredphotons.

Turning now to FIG. 5, a process for imaging in accordance with thepresent invention will be described with respect to exemplary stepsembodied as a flow chart 100. The process begins with the performance ofa CT imaging process, at process block 102, such as using theabove-described CT systems, including PC or ED CT systems. In thissense, it is contemplated that the CT imaging process may be atraditional “multi-energy” CT imaging process, including, for example,the common “dual-energy” and “dual-energy, dual-source” CT imagingprocesses. In addition, it is contemplated, for example, when using theaforementioned PC or ED CT systems, that the CT process may deviate fromtraditional “multi-energy” CT acquisitions, so long as the ability todiscriminate and bin the acquired data based on energy is maintained. Tothis point, at process block 104, the acquired CT data is assembled orassigned, as described above, into bins that serve to divide theacquired CT data into spectral data sets.

As noted above, the present invention recognizes that these spectraldata sets are, in essence, multi-dimensional data sets, where one of thedimensions spans the energy series. For example, if the acquired CT datais three dimensional (3D) in the sense of acquiring CT in three spatialdimensions, spectral data sets are treated as four dimensional (4D) datasets. Similarly, if the acquired CT data is two dimensional (2D) in thesense of acquiring CT data in two spatial dimensions or the acquired CTdata is 3D in the sense of acquiring CT data in 3 spatial dimensions andacross a time series, the spectral data sets represent 3D or 5D datasets, respectively, in the context of the present invention. The presentinvention further recognizes that a high degree of correlation existsbetween the spectral data sets due to the fact that the data setspertain to the same patient anatomy. Using this information, the presentinvention, at process block 106, forms a “conglomerate data set” thatincludes data spanning the various spectral data sets assembled atprocess block 104. For example, data from bins associated with 20-40keV, 40-60 keV, 60-80 keV, and the like can serve as the bins acrosswhich the conglomerate data set spans. Put another way, the conglomeratedata set and, any conglomerate image, is formed from CT from a varietyof different energy bins used to segregate the CT data acquired usingthe multi-energy imaging process at process block 102. As will beexplained in detail, this conglomerate data set and/or any associatedconglomerate image formed therefrom may be used to reconstruct an energyseries of CT images at process block 108 that has substantially improvedmaterial differentiation achieved without a substantial noise penaltyincurred using traditional methods.

In accordance with some implementations, the “conglomerate data set” orconglomerate image may use a substantial amount or even most or allx-ray photons received and associated with the energy bins. By using aconglomerate data set during the reconstruction of individual energyimages from the data associated with each individual energy bin, thetrade-off between bin number (or width) and image noise is substantiallyreduced or, for clinical purposes, effectively eliminated. This providespreviously-unachievable flexibility to choose energy bins based uponsignal optimization so that the best materialidentification/differentiation information can be achieved without theimages succumbing to noise. Therefore, the constraints presented inFIGS. 1 and 2 can be managed in a clinical setting, without paying thesignificant penalty of either increased image noise or increased patientdose.

A variety of methods are contemplated for creating a conglomerate dataset and reconstructing an energy series of images using the conglomeratedata set or image. For example, two methods include a HighlY constrainedback-PRojection (HYPR) processing and reconstruction and a Prior ImageConstrained Compressed Sensing (PICCS), Non-convex PICCS, and multi-bandfiltration. Exemplary HYPR and HYPR-based methods are described in U.S.Pat. No. 7,519,412, which is incorporated herein by reference. ExemplaryPICCS and PICCS-based methods are described in US Patent ApplicationPublication No. 2009/0161932, which is incorporated herein by reference.

In accordance with one aspect of the invention, the “conglomerate image”may be provided by applying the concepts creating a “composite image” asdescribed within the context of HYPR, to use the acquired photon datathat spans multiple energy bins to form the conglomerate data set. HYPRand its modified version, HYPR-LR, allow the reconstruction of a time orother series images from highly undersampled data set using a “compositeimage” built from multiple time series of images. HYPR-LR andHYPR-LR-related methods are described in U.S. Patent Publication No.2008-0219535, which is incorporated herein by reference. TheseHYPR-based concepts, which were first applied to a time series ofimages, can be used in accordance with the present invention toreconstruct an energy series of images formed of separate images, aswill be described.

It has been demonstrated that the signal to noise ratio (SNR) ofHYPR-reconstructed images are determined by the composite image insteadof the single frame image, which improves the SNR of images at each timeframe. The HYPR technique and general concepts thereof can be adaptedinto spectral CT imaging to provide a “conglomerate data set” or“conglomerate image” to improve SNR of images at each individual energybin. In this regard, the “composite image” of HYPR and the concepts forcreation of the “composite image” may be extended to the above-describedenergy series to form a “conglomerate data set” or “conglomerate image”in the context of the present invention.

A “conglomerate data set” or “conglomerate image” in the context of thepresent invention may be formed using the concept of a “composite image”in HYPR by using x-ray photons acquired across the energy spectrum andrespective bins and, thereby, a “composite data set” or “conglomeratedata set” can be formed having the SNR that is independent of the numberof energy bins. That is, as the SNR of HYPR images is determined by thecomposite image, in the present invention, images at each energy bintherefore have an SNR equivalent to that obtained with all x-rayphotons. For example, these conglomerate images can be generated usingan averaging or may be generated by applying different weighting factorsto each energy bin, which may improve image quality. By reconstructingthe individual energy images associated with each energy bin using aconglomerate image and a HYPR-based reconstruction, the tradeoff betweennumber of energy bins and image noise in each energy bin issubstantially reduced or eliminated.

Turning to FIG. 6, a specific example, using the HYPR-LR concept of a“composite image” and reconstruction, is provided by way of a flow chart200. The exemplary process using HYPR-based techniques begins byperforming a CT imaging process. As explained above, it is contemplatedthat the CT imaging process may be a traditional “multi-energy” CTimaging process, including, for example, the common “dual-energy” and“dual-energy, dual-source” CT imaging processes. In addition, it iscontemplated, for example, when using the aforementioned PC or ED CTsystems, that the CT process may deviate from traditional “multi-energy”CT acquisitions, so long as the ability to discriminate and bin theacquired data based on energy is maintained. To this point, at processblock 204, the acquired CT data is assembled or assigned, as describedabove, into bins that serve to divide the acquired CT data into spectraldata sets.

As previously described with respect to FIG. 5, a conglomerate data setis then formed from the spectral data sets at process block 206.However, within this example utilizing HYPR-based techniques for formingthe conglomerate data set and reconstructing the energy series ofimages, a conglomerate image I_(C) may be produced by averaging datafrom some or all of the energy bins. In some cases, all of the data fromall of the bins may be used to ensure that all available photons areincluded in the conglomerate image to, thereby, produce the lowest imagenoise. At process block 208 a filter operation is then performed on bothindividual energy data sets or images I_(E) and the composite dataset/image l_(C). At process block 210, a weighting or weighting image isobtained as the ratio between individual energy data sets or imagesI_(E) and the composite data set/image I_(C) , as filtered. At processblock 212, HYPR “processing” or reconstruction, for example, HYPR-LRprocessing, can then be used to form an enhanced spectral image I_(HE)as the multiplication of the weighting image and the conglomerate image.Mathematically, the HYPR-LR algorithm can be expressed as:

$\begin{matrix}{{I_{HE} = {\frac{I_{E} \otimes K}{I_{C} \otimes K} \cdot I_{C}}};} & {{Eqn}.\mspace{14mu} 1}\end{matrix}$

where K is a low-pass filter kernel. A kernel, such as a 7×7 pixeluniform square kernel or other desirable kernel, can be used. The symbol“{circle around (x)}” represents a convolution process.

Using error propagation theory, image noise after such HYPR-LRprocessing has been derived in MRI images and CT images. It can beexpressed as:

$\begin{matrix}{{\sigma_{I_{HE}}^{2} \approx {\sigma_{I_{C}}^{2} + \frac{\sigma_{I_{E}}^{2}}{N_{K}} + \frac{2\; \sigma_{I_{C}}^{2}}{N_{K}}}};} & {{Eqn}.\mspace{14mu} 2}\end{matrix}$

where σ_(I) _(HE) ² is the noise variance in the HYPR-LR processedimages at energy bin E, σ _(C) ² is the noise variance in compositeimage, σ_(I) _(E) ² is the noise variance in individual energy binimage, and N_(K) is the number of pixels used in the filter kernel. Itcan be observed that the noise variance of HYPR-LR images is mainlydetermined by that of the composite images and this translates to thepresent invention and the use of HYPR-LR with a “conglomerate image.”Thus, in the present invention, noise variance is mainly determined bythat of the composite images and only weakly depends on that ofindividual energy bin image. This relationship is more obvious if theenergy bins are selected in such a way that similar noise is measured ineach individual energy bin (σ_(I) _(E) ²=N_(E)×σ_(I) _(C) ²). In thisscenario, Eqn. 2 can be rewritten as:

$\begin{matrix}{{\sigma_{I_{HE}}^{2} \approx {\sigma_{I_{C}}^{2}( {1 + \frac{N_{E} + 2}{N_{K}}} )}};} & {{Eqn}.\mspace{14mu} 3}\end{matrix}$

where N_(E) is the number of energy bins. In practice, N_(E) (number ofenergy bins) is usually much smaller than N_(K) due to physicallimitations in the detector hardware. Therefore, the noise variance ofimages σ_(I) _(HE) ² in the enhanced energy series is expected to beclose to that of conglomerate image.

It is noted that the size of the convolution kernel used for the HYPR-LRprocessing has an impact on CT number accuracy and image noisereduction. As seen from Eqns. (2) and (3), image noise is reduced askernel size increases, although the incremental noise reductiondiminishes as the noise level approaches the noise level of compositeimage. A very large kernel could affect CT number accuracy andconsequently energy specific information. A 7×7 pixel uniform filterkernel has been demonstrated as a reasonable choice to reduce imagenoise without affecting CT number accuracy or spatial resolution. Onelimitation of the HYPR-LR algorithm is that it prefers imaging scenarioswithout substantial motion between energy-specific images. However, inmost spectral CT systems (e.g. photon-counting, detector-based ordual-source CT systems), imaging data at different beam energies areacquired simultaneously. Therefore, motion is of minimal concern. Forsystems in which substantial delay is expected between different energydata acquisitions (e.g. dual energy CT using two separate scans), motionmight be a concern and HYPR-LR techniques should be adjusted to accountfor motion.

To demonstrate this effect, CT numbers and noise variances can bemeasured inside three circular ROIs placed at regions representingcalcium, water, and soft tissue. The measurement can be conducted onimages reconstructed using both commercial software and HYPR-LR forcomparison. Noise reduction using HYPR-LR is then calculated, andcorresponding dose reduction is estimated based upon the relationshipbetween radiation dose and image noise. Images of a semi-anthropomorphicthoracic phantom scanned at 80, 100, 120, and 140 kVp are shown in FIGS.7A and 7B. Specifically, FIG. 7A shows images reconstructed without theaid of the present invention and FIG. 7B shows images processed with thepresent invention and HYPR providing the composite image as theconglomerate image. Significant noise reduction was observed after HYPRprocessing for images at each beam energy. Due to the high image noisein the original image, the water-equivalent rod, which is labeled inFIG. 7B, is almost not differentiable in the images of FIG. 7A. On theother hand, in the images of FIG. 7B, the water-equivalent rod isclearly seen. This distinction between the images of FIGS. 7A and 7B isdue to the use of a conglomerate image that reduces noise and increasesSNR in the resulting images.

Thus, HYPR-LR processing applied in the context of the present inventiondid not alter spatial resolution or energy-specific CT numbers. Theeffectiveness of the present invention readily translates to bothphoton-counting, detector-based and integrating, detector-based CTsystems using numerical simulations, phantom, and patient studies.Numerical simulations demonstrated a 36-76% noise reduction using thepresent invention with HYPR-LR processing in the energy domain comparedto standard FBP reconstruction for the case when 6 energy bins wereused. The percent noise reduction changed when different numbers ofenergy bins or different bin widths were used.

Turing now to FIG. 8, the CT number (as mean) and image noise (asstandard variation) of calcium, water, and tissue equivalent are shown.The energy dependent CT numbers for different materials makes itpossible for material differentiation using spectral CT. The same CTnumbers were maintained after applying the present invention comparedwith the original CT numbers. Noise reduction using the presentinvention can be observed by the smaller error bars compared with thosein original images. The percentage of noise reduction is shown in TableI.

TABLE I Percent noise reduction kVp Ca Water Tissue Average 80 50% 51%51% 51% 100 43% 37% 44% 41% 120 49% 53% 37% 46% 140 45% 45% 44% 45%

Overall, an approximately 50 percent noise reduction is achieved using aconglomerate image and HYPR-based reconstruction. Based upon therelationship between noise and radiation dose (Dose˜1/noisê2), this isequivalent to a factor of 4 dose reduction given the same image noise.These results could be interpreted as noise reduction given the sameradiation dose, dose reduction given the same image noise, or thecombination of these two.

CT numbers were well preserved using the present invention and imagenoise was comparable to that of the composite image using all photons,significantly reduced compared with standard filter backprojection (FBP)algorithms. This breaks the trade-off between image noise and energy binsize and/or numbers, which allows flexibility to use optimal energy binsfor best spectral imaging.

Radiation dose reduction can be achieved with the reduced image noiseusing the present invention. As addressed above, other processingtechniques for forming the conglomerate data set and reconstructing theenergy series of images are contemplated, including Prior ImageConstrained Compressed Sensing (PICCS) image reconstruction. In thiscase, the conglomerate image may serve as the so-called “prior image” ofPICCS in PICCS-based reconstruction.

As a simple special case, dual-energy CT, which is now clinically usedfor stone composition differentiation, bone removal in CT angiography,gout detection, and iodine quantitifcation, can also benefit from thisapproach. In current clinical dual-energy CT implementations, x-rayphotons are approximately equally distributed into the low and highenergy scans. Each image set then uses only one half of the totalradiation dose delivered to the patient and has higher noise comparedwith a conventional (single energy) CT that uses the full dose to thepatient. The method of the present invention improves the noise propertyof each image set to that of the conventional (single energy) full doseimages, while preserving energy-selective information for dual energyprocessing.

Thus, dual-energy CT, which is one specific implementation of the moregeneral spectral CT, has been shown to provide clinically usefuldiagnostic information beyond what is available with conventional singleenergy CT. Using the present invention, noise can be reduced in both thelow- and high-energy images of a dual energy CT exam. For example,HYPR-LR processing may be conducted in low- and high-energy imagesbefore material decomposition. Noise can also reduced aftermaterial-specific processing is performed to subtract iodine signal(i.e. to create a virtual, non-contrast-enhanced dataset). Because noiseis reduced in both the low- and high-energy images, any other dualenergy processing algorithms, such as those performed to simulatemonoenergetic images, also benefit from this invention. Furthermore, itis contemplated that the above-described methods may be used to processimage data after material decomposition has been performed. In suchcase, a “enhanced” material-specific image may be generated inaccordance with the above-described processes.

In CT imaging, image noise is inversely proportional to the number ofavailable photons. Therefore, image noise in spectral CT for eachindividual energy bin is determined by the number of photons fallinginside the energy range. The tradeoff between using more energy bins(providing more energy specific measurements) and decreasing image noise(providing more photons) has to be considered. Noise is primarilydetermined by the conglomerate image, which may use all the appliedphotons instead of only the photons falling within a single energy bin.This has been demonstrated using 2, 4, and 6 energy bins with a dualenergy CT patient exam (2 bins), conventional CT phantom scans (4 bins),photon-counting μ-CT (6 bins) and numerical simulations (6 bins). More(greater than 2) energy bins could be used without substantiallyincreasing image noise. However, the number of energy bins cannot beincreased arbitrarily for a number of reasons. First, the maximal numberof energy bins is mainly determined by the detector hardware. Second,based on Eqns. (2) and (3), increasing the number of bins will slightlyincrease image noise, especially when the number of bins becomescomparable to the number of pixels in the convolution kernel. Third, theincremental benefit of increasing the number of energy bins might not besignificant once it reaches a certain level. Currently, many photoncounting detectors operate using 2-8 energy bins. In this range, theincrease in image noise using more energy bins is negligible afterprocessing, and the tradeoff between more energy bins (more energyspecific information) and more photons (less noise) is practicallyavoided.

In conclusion, it has been demonstrated the ability to substantiallyreduce noise in spectral CT images by exploiting informationredundancies in the energy domain. The ability to reduce image noise tothe level of a conventional CT image that uses all available photonseliminates the need to trade off image noise (and hence, patient dose)and the number and width of energy bins. This approach provides maximalflexibility for energy beam optimization without paying a price in termsof increased noise or dose.

The present invention has been described in accordance with theembodiments shown, and one of ordinary skill in the art will readilyrecognize that there could be variations to the embodiments, and anyvariations would be within the spirit and scope of the presentinvention. Accordingly, many modifications may be made by one ofordinary skill in the art without departing from the spirit and scope ofthe appended claims.

1. A method for creating a series of images acquired using amulti-energy computed tomography (CT) imaging system having a pluralityof energy bins, the method comprising the steps of: (a) acquiring aseries of energy data sets, each energy data set associated with atleast one of the energy bins; (b) producing a conglomerate data setusing at least a plurality of the energy data sets; and (c) using theconglomerate data set, generating at least one of an enhancedmaterial-specific image and an enhanced energy series of images, eachimage corresponding to at least one of the energy data sets.
 2. Themethod of claim 1 wherein the conglomerate data set includes data fromeach energy data set in the series of energy data sets.
 3. The method ofclaim 1 wherein step (b) includes producing a HYPR-based composite imageby combining corresponding views across each of the series of energydata sets acquired in step (a) and step (c) includes reconstructing theenergy series of images by normalizing the energy data set withinformation derived from the composite image and by multiplying thenormalized result with the composite image.
 4. The method of claim 1wherein the series of energy data sets is acquired using at least one ofa photon counting and energy discriminating CT detector.
 5. The methodof claim 1 wherein step (c) includes at least one of reconstructingmaterial decomposition images using the series of energy data sets andgenerating the enhanced material-specific image using the conglomeratedata set.
 6. The method of claim 1 further comprising weighting each ofthe energy-selective data sets.
 7. A method for creating an energyseries of images acquired using a computed tomography (CT) imagingsystem, the method comprising the steps of: (a) acquiring a series ofenergy-selective data sets, each energy-selective data set associatedwith energy bin; (b) producing a conglomerate data set from theenergy-selective data sets including data associated with at least aplurality of the energy bins; (c) weighting each of the energy-selectivedata sets using the conglomerate data set; and (d) reconstructing anenhanced energy series of images, where each image in the enhancedenergy series of images corresponds to at least one of the energy datasets.
 8. The method of claim 7 wherein step (b) includes producing theconglomerate data set from all data in the energy-selective data sets.9. The method of claim 7 wherein step (c) includes filtering the seriesof energy-selective data sets and the conglomerate data set.
 10. Themethod of claim 9 wherein the filtering includes applying a low-passfilter kernel.
 11. The method of claim 7 wherein step (c) includesforming a weighting data set as the ratio of a given data set in theseries of energy-selective data sets to the conglomerate data set. 12.The method of claim 7 wherein step (d) includes performing a HYPR-basedreconstruction using the conglomerate data set and the series of energyselective data sets.
 13. The method of claim 7 wherein step (d) includesapplying a HYPR-LR-based processing mathematically expressed as:${I_{HE} = {\frac{I_{E} \otimes K}{I_{C} \otimes K} \cdot I_{C}}};$where I_(HE) represents an image in the enhanced energy series ofimages, I_(E) represents individual energy data sets in the series ofenergy-selective data sets, I_(C) represents the conglomerate image dataset, K represents a filter kernel, and {circle around (×)} represents aconvolution process.
 14. The method of claim 7 wherein step (d) includesapplying a HYPR-LR-based processing to form the enhanced energy seriesof images as the multiplication of a weighting used in step (c) and theconglomerate image data set.
 15. The method of claim 7 wherein step (a)includes acquiring the series of energy-selective data sets using amulti-energy CT imaging system.
 16. A computed tomography (CT) imagingsystem comprising: an x-ray source configured to emit x-rays toward anobject to be imaged; a detector configured to receive x-rays that areattenuated by the object; a data acquisition system (DAS) connected tothe detector to receive an indication of received x-rays; a computersystem coupled to the DAS to receive the indication of the receivedx-rays and programmed to: segregate the indication of the receivedx-rays into a series of energy data sets based on an energy levelassociated with received x-rays; produce a conglomerate image data setusing data from at least a plurality of the energy data sets; andreconstruct at least one of an enhanced material-specific image and anenhanced energy series of images, each image in the at least one of theenhanced material-specific image and enhanced energy series of imagescorresponding to at least one of the energy data sets, using the seriesof energy data sets and the conglomerate image data set.
 17. The CTimaging system of claim 16 wherein the detector includes at least one ofa photon counting and energy discriminating CT detector.
 18. The CTimaging system of claim 16 wherein the conglomerate image data set isformed using all data in the plurality of the energy data sets.
 19. TheCT imaging system of claim 16 wherein the computer is further programmedto form a weighting image data set as the ratio of a given data set inthe plurality of the energy data sets to the conglomerate image dataset.
 20. The CT imaging system of claim 16 wherein the computer isfurther programmed to performing a HYPR-based reconstruction toreconstruct the enhanced energy series of images.
 21. The CT imagingsystem of claim 16 wherein the computer is further programmed to performa HYPR-LR-based processing to reconstruct the enhanced energy series ofimages as a multiplication of a weighting and the conglomerate imagedata set.
 22. A method for creating a series of images acquired using amulti-energy imaging system having a plurality of energy bins, themethod comprising the steps of: (a) acquiring a series ofenergy-selective data sets, each energy-selective data set associatedwith at least one of the energy bins of the imaging system; (b)producing a conglomerate data set using at least a plurality of theenergy-selective data sets; and (c) using the conglomerate data set,generating at least one of an enhanced material-specific image and anenhanced energy series of images.
 23. The method of claim 22 wherein theconglomerate data set includes data from each energy data set in theseries of energy data sets.
 24. The method of claim 22 wherein step (b)includes producing a HYPR-based composite image by combiningcorresponding views across each of the series of energy data setsacquired in step (a) and step (c) includes reconstructing the energyseries of images by normalizing the energy data set with informationderived from the composite image and by multiplying the normalizedresult with the composite image.
 25. The method of claim 22 wherein step(c) includes at least one of reconstructing material decompositionimages using the series of energy data sets and generating the enhancedmaterial-specific image using the conglomerate data set.